Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods

Sijil Pelajaran Malaysia (SPM) or Malaysia Certificate of Education is a national examination required to be seated by all Form Five secondary school students in Malaysia. This examination is compulsory for them to pursue post-secondary education. However, students’ performance on this examination...

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Main Author:	Muhammad Haziq, Bin Roslan
Format:	Thesis
Language:	English
Published:	2022
Subjects:	LB1603 Secondary Education High schools
Online Access:	http://ir.unimas.my/id/eprint/39214/5/Master%20Sc%20Thesis_Muhammad%20Haziq%20Roslan%20-%20fulltext.pdf
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spelling	my-unimas-ir.392142023-03-13T03:33:14Z Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods 2022-08-08 Muhammad Haziq, Bin Roslan LB1603 Secondary Education. High schools Sijil Pelajaran Malaysia (SPM) or Malaysia Certificate of Education is a national examination required to be seated by all Form Five secondary school students in Malaysia. This examination is compulsory for them to pursue post-secondary education. However, students’ performance on this examination particularly in English and Mathematics subjects has been concerning. According to the literature, advances in educational technology can improve students’ performance. Therefore, this study attempts to predict Form Four students’ SPM performance in English and Mathematics subjects using data mining (DM) techniques. Three main attributes namely students’ past academic performance, demographics, and psychological attributes were scrutinised to identify their impact on the prediction. Moreover, the predictive performance of the DM techniques was evaluated to find the best technique for predicting students’ SPM performance. The relationship between students’ SPM performance in English and Mathematics subjects was also examined. This study found that by using Decision Tree (DT) rules, the characteristics of students with low, moderate, and high performance in English and Mathematics subjects could be identified. Next, DT and Nave Bayes (NB) had the best predictive performance among the DM approaches in predicting students’ SPM performance in English and Mathematics respectively. Moreover, there is a connection between students’ SPM English and Mathematics performance. The findings may provide stakeholders with new insight into how to improve students’ performance in these subjects. Educators can intervene early with students who are at risk of receiving low performance for these subjects in SPM. Education and Information Technologies 2022-08 Thesis http://ir.unimas.my/id/eprint/39214/ http://ir.unimas.my/id/eprint/39214/5/Master%20Sc%20Thesis_Muhammad%20Haziq%20Roslan%20-%20fulltext.pdf text en validuser masters University Malaysia Sarawak Faculty of Cognitive Sciences and Human Development Malaysian Ministry of Higher Education, Fundamental Research Grant Scheme, FRGS/1/2020/SS10/UNIMAS/01/1, and UNIMAS Zamalah Scholarship
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	LB1603 Secondary Education High schools
spellingShingle	LB1603 Secondary Education High schools Muhammad Haziq, Bin Roslan Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods
description	Sijil Pelajaran Malaysia (SPM) or Malaysia Certificate of Education is a national examination required to be seated by all Form Five secondary school students in Malaysia. This examination is compulsory for them to pursue post-secondary education. However, students’ performance on this examination particularly in English and Mathematics subjects has been concerning. According to the literature, advances in educational technology can improve students’ performance. Therefore, this study attempts to predict Form Four students’ SPM performance in English and Mathematics subjects using data mining (DM) techniques. Three main attributes namely students’ past academic performance, demographics, and psychological attributes were scrutinised to identify their impact on the prediction. Moreover, the predictive performance of the DM techniques was evaluated to find the best technique for predicting students’ SPM performance. The relationship between students’ SPM performance in English and Mathematics subjects was also examined. This study found that by using Decision Tree (DT) rules, the characteristics of students with low, moderate, and high performance in English and Mathematics subjects could be identified. Next, DT and Nave Bayes (NB) had the best predictive performance among the DM approaches in predicting students’ SPM performance in English and Mathematics respectively. Moreover, there is a connection between students’ SPM English and Mathematics performance. The findings may provide stakeholders with new insight into how to improve students’ performance in these subjects. Educators can intervene early with students who are at risk of receiving low performance for these subjects in SPM.
format	Thesis
qualification_level	Master's degree
author	Muhammad Haziq, Bin Roslan
author_facet	Muhammad Haziq, Bin Roslan
author_sort	Muhammad Haziq, Bin Roslan
title	Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods
title_short	Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods
title_full	Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods
title_fullStr	Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods
title_full_unstemmed	Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods
title_sort	prediction of students’ performance in spm english and mathematics using data mining methods
granting_institution	University Malaysia Sarawak
granting_department	Faculty of Cognitive Sciences and Human Development
publishDate	2022
url	http://ir.unimas.my/id/eprint/39214/5/Master%20Sc%20Thesis_Muhammad%20Haziq%20Roslan%20-%20fulltext.pdf
_version_	1783728511208390656

Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods

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